8568results about "Tunnels" patented technology

The invention provides a stratum disturbance analysis method and system under a shield construction coupling effect. Cutting vibration spectrum data are collected in real time through a cutter vibration sensor, and a feature fingerprint database containing a vibration energy distribution mode and a critical grouting interval is constructed in combination with soil parameters. And aligning the vibration spectrum with the soil bin pressure in a space-time manner, and generating a disturbance field distribution diagram for displaying an energy gradient distribution curve and a stress diffusion path topology. And matching the energy distribution curve and correcting formation interface propagation parameters through the feature matching network optimized by transfer learning, and outputting a formation type identification result and disturbance dynamic parameters. And based on the mapping relation between the parameters and the critical grouting interval, the ground surface displacement data are linked to dynamically regulate and control the grouting pressure, graded early warning and parameter regulation instructions are generated, and a stratum disturbance monitoring-regulation and control closed loop is formed. According to the technical scheme, dynamic optimization of the grouting pressure is achieved, and the stratum deformation risk caused by shield construction is remarkably reduced.

A multi-objective optimization method and system for a shield construction parameter. The method comprises: acquiring an initial shield construction parameter; and optimizing the shield construction parameter by using a multi-objective optimization algorithm, during the optimization of the shield construction parameter, on the basis of the shield construction parameter, obtaining a tunneling speed by means of performing prediction on the basis of random forest, calculating a shield tunneling energy consumption ratio on the basis of the tunneling speed and the shield construction parameter, and taking the maximization of the tunneling speed and the minimization of the shield tunneling energy consumption ratio as two fitness functions of the multi-objective optimization algorithm. The multi-objective optimization method and system for a shield construction parameter take construction efficiency into consideration and reduce the construction cost while ensuring construction safety.

The invention relates to the field of shield tunneling, and discloses a shield tunneling control method and system based on a rock-soil particle motion trail, and the method comprises the steps: fusion deployment of various types of high-precision sensors, intelligent anti-noise data collection and preprocessing, precise physical model construction, efficient numerical simulation optimization, and multi-parameter comprehensive detection and correlation analysis. And real-time data-driven adaptive control model construction, control instruction rapid generation and transmission, closed-loop feedback and continuous optimization are realized. According to the method, multiple links including fusion deployment of multiple types of high-precision sensors, intelligent anti-noise processing, physical model construction and the like are covered, all the links have a synergistic effect, data can be accurately obtained, data quality can be guaranteed, a simulation foundation can be provided, comprehensive evaluation, intelligent control and rapid instruction transmission can be achieved, continuous optimization is achieved through closed-loop feedback, and the method is suitable for large-scale popularization and application. And finally, the construction level in all aspects is improved, and complex engineering requirements are met.

The invention discloses a large-diameter shield tunneling parameter optimization method and system based on digital twinning, and relates to the technical field of shield tunnel numerical simulation, machine learning and digital twinning, and the method comprises the steps: obtaining shield tunnel construction site data, and constructing a virtual digital twinning body; building a training sample set based on virtual numerical twinborn generation data; training an agent model by using the training sample set; during construction, actual stratum response data and tunneling parameter data are obtained in real time, the real-time data are input into the soil mass parameter inversion agent model, and the optimal value of the soil mass parameter is rapidly output; the optimal value is input into the virtual digital twin, and dynamic real-time updating of numerical simulation is carried out; and on the basis of the updated soil parameter data and the tunneling parameter data in the next construction step, stratum response prediction data are obtained through the stratum response prediction agent model, so that the tunneling parameter data are adjusted and optimized. According to the invention, more accurate numerical simulation and safe and efficient tunneling of the shield tunnel can be realized.

The invention discloses a seabed karst stratum tunnel shield data monitoring and analyzing method and system. The method comprises the steps that shield construction mechanical parameters, karst stratum acoustic emission signals and seawater osmotic pressure data are synchronously collected; shield construction mechanical parameters, karst stratum acoustic emission signals and seawater osmotic pressure data are subjected to seepage-stress coupling constraint, a hydraulic connectivity strength index is generated through fracture network seepage tensor inversion, and the filler migration risk level is judged based on pore water pressure dynamic mutation characteristics. And fusing the cutterhead torque frequency spectrum characteristic and the propulsive force fluctuation characteristic to generate a shield disturbance entropy value. According to the method, the hydraulic connectivity index is generated through seepage stress coupling inversion, the grouting path and the rheological parameters are dynamically optimized based on the fracture probability field, shield tunneling, seepage sudden change and grouting prevention and control cooperation is achieved, and the problem of prevention and control disjunction is radically solved.

The present invention relates to the technical field of tunnel rockburst microseismic monitoring, and provides a strain-structural plane slip-type rockburst failure mechanism analysis method for a drill-and-blast method tunnel. The present invention collects strain-structural plane slip-type rockburst microseismic data during tunnel construction; in light of engineering geological survey data, performs microseismic monitoring and data analysis on strain-structural plane slip-type rockburst that has occurred in a tunnel, divides a strain-structural plane slip-type rockburst risk area, and determines a microseismic rockburst early warning area; and, by means of these data, deeply understands a microseismic activity law of strain-structural plane slip-type rockburst of deep-buried drill-and-blast method tunnels, discriminates respective functions of tensioning and shearing in rock failure processes, comprehensively analyzes the failure mechanism of strain-structural plane slip-type rockburst, and further obtains the failure mechanism of strain-structural plane slip-type rockburst of the deep-buried drill-and-blast method tunnels, providing an important theoretical basis for predicting and preventing rockburst disasters.

The invention relates to the technical field of tunnel engineering intelligent construction, and discloses a hard rock TBM shield tunneling machine auxiliary tunneling parameter dynamic adaptation regulation and control system, which comprises a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network and a high-precision multi-source sensing network, the TBM-geological environment digital twin predicts the tunneling short-term trend based on the high-fidelity physical simulation and data assimilation technology; and the multi-modal deep learning collaborative decision-making module deeply fuses real-time and prediction data and generates an optimal parameter solution set through a network trade-off tunneling multi-conflict target based on Pareto optimization. And the system executes a decision and forms closed-loop feedback through a parameter dynamic adaptation and adaptive learning module, and continuously optimizes a self model and a knowledge base. According to the method, passive response of TBM tunneling is converted into active pre-judgment, the decision accuracy, the construction safety and the comprehensive tunneling efficiency under the complex working condition are remarkably improved, and the method has the sustainable evolution capacity.

An asymmetric high stress tunnel rock burst micro-seismic sensor arrangement method, relating to the technical field of tunnel engineering. On the basis of tunnel surrounding rock geological data, three-dimensional numerical simulation is carried out for asymmetric high stress tunnel stability on the basis of asymmetric in-situ rock geo-stress distribution and tunnel section geometric characteristics, and asymmetric high stress tunnel energy is calculated; the location of a rock burst risk area is determined; micro-seismic sensors are arranged in front of and behind a stress concentration area; the area at a high risk and nearby areas thereof are always incorporated in two micro-seismic sensor arrays arranged in front of and behind a tunnel face. The micro-seismic sensors work cooperatively and are staggered in space, the mounting and arrangement of the micro-seismic sensors changes forwards and backwards along with the stress concentration area, and the micro-seismic sensors always keep a certain distance range with the stress area. The tunnel stress concentration area and more nearby effective micro-crack source signals can be captured by the micro-seismic sensors, thereby improving the precision of locating a micro-seismic source and laying a foundation for accurate prediction and forecast of deep-buried hard rock tunnel disasters.

The invention discloses an underground tunneling inertial navigation system and method for multi-source information fusion, and relates to the technical field of underground tunneling navigation, and the system comprises a plurality of functional units such as a shape memory alloy self-calibration inertial module and a magnetorheological fluid intelligent vibration reduction platform. Multi-source data fusion processing is realized through technical means of hardware self-calibration, full-band vibration suppression, dynamic weight distribution and the like. In the aspect of inertial navigation, the eight-hour positioning error is smaller than 5 cm and is remarkably improved compared with a traditional system; the positioning precision under complex working conditions reaches + / -10cm / 100m, which is obviously improved compared with a fixed weight fusion method. And the delay time is less than 50ms during multi-machine collaborative operation, and the supporting efficiency is improved by 35%. Meanwhile, geological anomalies can be responded in real time, the fault passing efficiency is improved by 60%, the coal rock cutting error rate is reduced by 70%, and navigation continuity is guaranteed when the sensor fails. According to the invention, the problem of navigation of underground tunneling is effectively solved, and the safety, high efficiency and intelligent level of underground tunneling operation are greatly improved.

The invention relates to the technical field of shield tunnel construction, and particularly discloses a narrow space limit radius shield tunnel intelligent regulation and control method which comprises the steps that shield tunneling machine state information is obtained; acquiring formation pressure, water content and soil deformation data to obtain formation characteristic information; based on the stratum characteristic information, an optimal construction adjustment strategy is obtained through analysis; on the basis of the optimal construction adjustment strategy, deep reinforcement learning is combined with fuzzy PID control to calculate the optimal tunneling track of the shield tunneling machine, and a narrow space limit radius adaptability construction scheme is obtained; according to the method, the track of the shield tunneling machine is optimized based on deep reinforcement learning in combination with fuzzy PID control, dynamic posture correction is achieved, and the deviation of the tunneling track is reduced. The attitude estimation precision is improved through multi-sensor data fusion, the shield attitude angle error is reduced, the manual intervention frequency is effectively reduced, and the construction efficiency is improved; automatic thrust and torque optimization control is achieved, posture sudden change caused by stratum change is avoided, and the stability of the shield tunneling machine is improved.

The invention discloses a multi-source collaborative advanced geological exploration method based on TBM construction, and belongs to the technical field of tunnel engineering geological exploration. A three-dimensional geological model is constructed through multi-scale collaborative operation of directional long exploration drilling, branch short exploration drilling and in-hole transient electromagnetic detection; the directional long exploration adopts a secondary drilling structure and a screw motor directional drilling technology to realize accurate control of a deep stratum, the branch short exploration realizes dynamic and rapid verification through a TBM integrated drilling machine, meanwhile, in-hole transient electromagnetic exploration adopts three-component measurement and an intelligent inversion algorithm to fill the blank of geological information between drill holes, and by means of mutual data correction of the three methods, the detection accuracy is improved. A three-in-one detection system of drilling, geophysical prospecting and intelligent analysis is formed, and then accurate advanced prediction is achieved, so that the problems that a traditional single detection technology is low in precision and insufficient in coverage range are effectively solved, the water inrush risk is remarkably reduced, construction safety is guaranteed, and the method is effectively suitable for deep tunnel TBM safety construction.

The invention discloses a shield tunneling intelligent auxiliary method based on stratum types. The method comprises the steps that parameters of a current shield tunneling machine in the tunneling process are collected; through Pearson's correlation coefficient calculation, correlation coefficients of all the parameters and the total thrust and the cutterhead torque are obtained respectively, and a feature combination is obtained; utilizing a K-means algorithm to calculate and obtain a stratum classification result which is clustered by taking a basic combination consisting of the total thrust and the cutterhead torque and a feature combination as a feature set; a cooperative training algorithm is adopted, and dug stratum classification based on the basic combination and the feature combination is obtained under semi-supervised learning; a machine learning algorithm is utilized to carry out learning training on types of excavated stratums, unknown stratum conditions in the tunneling direction are identified, and a reference range of total thrust and cutterhead torque is given. The method can be circularly executed in the shield tunneling process, the excavated stratum classification result is continuously updated, the method has the characteristics of practical feasibility, real-time performance and the like, and important auxiliary decision-making information can be provided for shield tunneling.

The invention relates to a shield tunneling machine tunneling error compensation control method and system, and relates to the field of tunneling operation, and the method comprises the steps: carrying out the time domain alignment and cutting region mapping alignment of a predicted soil feature distribution sequence and a predicted tool wear distribution sequence, and carrying out the integration to obtain a predicted operation state data sequence; and performing iterative optimization analysis on tunneling control parameters of the shield tunneling machine on the basis of the predicted operation state data sequence by taking the minimum overall tunneling operation error of the cutterhead as an expected target until a preset optimization scale is met, outputting an optimal tunneling parameter sequence, and controlling the shield tunneling machine to execute tunnel construction in a future time zone. According to the method, the technical problems of insufficient tunneling precision and low operation efficiency caused by the fact that a traditional method lacks a dynamic adjustment mechanism and cannot adjust tunneling control parameters in a targeted manner according to soil texture changes and cutter wear conditions can be solved; by effectively reducing the tunneling operation error, the accuracy and the operation efficiency of the shield tunneling machine in the tunneling process are remarkably improved.

The invention belongs to the field of mud cake removal of shield tunneling machines, and discloses a shield cutterhead residue soil property sensing and intelligent mud cake removal system and method, and the system comprises a sensing module, a modifier spraying module, a regulation and control module and an intelligent terminal; the sensing module is used for collecting mechanical, chemical and thermodynamic signals in a soil bin of the shield tunneling machine in real time; the modifier spraying module is used for intelligently regulating and controlling the proportion of a modifier so as to inhibit mud cake adhesion; the regulation and control module is used for dynamically optimizing a modifier proportioning parameter and a cutterhead tunneling strategy according to the sensing data; and the intelligent terminal is used for displaying the mud cake distribution state and receiving a manual intervention instruction. According to the method, three-dimensional form dynamic analysis and accurate targeted removal of the mud cake can be realized.

The invention discloses a real-time prediction method for muck improvement parameters of a shield under-crossing existing tunnel, and the method comprises the steps: carrying out the refined preprocessing of non-under-crossing section shield tunneling data, enabling an obtained data set to be used for the training of a machine learning integrated prediction model, and obtaining a muck improver dosage integrated prediction model; meanwhile, slump experiments are carried out for different types of stratums, the optimal water content and foam injection ratio parameter range is determined, and a reliable decision basis is provided for a shield driver. The quality of a data set and the prediction precision of a machine learning model are remarkably improved through a finer data preprocessing means; the machine learning integrated prediction model fuses learning capabilities of different machine learning models, so that the prediction precision of the model is further improved; and machine learning and experimental means are combined to realize accurate and rapid prediction of the use amount of the modifier, so that safe and smooth tunneling of the shield under the existing tunnel is ensured, and the method has certain timeliness and economical efficiency.

The invention provides a shield adaptability adjusting method based on a stratum structure, which comprises the following steps: acquiring geological parameters of a construction area of a shield tunneling machine through drilling and geophysical prospecting, and arranging sensors at key positions of the shield tunneling machine to acquire construction parameters of the shield tunneling machine in real time; a convolutional neural network CNN is adopted to analyze the geological parameters to identify stratum features, and a geological model is constructed based on the identified stratum features; constructing a disaster risk index system, and predicting a disaster risk based on the geological model and the construction parameters by adopting Logistic regression and a random forest algorithm; according to the predicted disaster risk, the cutterhead configuration of the shield tunneling machine is dynamically adjusted, and tunneling parameters are optimized or a muck improvement scheme is adopted; by monitoring geological parameters, construction parameters and environmental changes in real time, a multistage early warning mechanism is adopted to trigger emergency response measures, and construction safety is guaranteed. According to the method, the safety and efficiency of shield construction can be improved, the geological disaster risk is reduced, and reliable technical guarantee is provided for underground space development.

The disclosure relates to the technical field of tunnel excavation, in particular to a non-blasting tunnel boring machine and a tunneling method. The tunneling machine comprises a vehicle-mounted device (1), a rotational driving device (2), a translation device (40), a working disc (3), a foundation trench excavation device (4), a drilling and splitting device (5), a hazard removal device (50), a dust suppression device (6) and a material feeding device (7). The rotational driving device (2) is arranged on the vehicle-mounted device (1), the working disc (3) is connected with the rotational driving device (2), the rotational driving device (2) drives the working disc (3) to rotate around a center of the working disc, the foundation trench excavation device (4) is arranged on the working disc (3), the drilling and splitting device (5) comprises a drilling machine (51) and a rock splitter (52) both arranged on the working disc (3), the drilling machine (51) is suitable for drilling a hole in a tunnel face in an annular crack arrest groove, the rock splitter (52) is suitable for splitting the tunnel face, the hazard removal device (50) is arranged on the working disc (3), the dust suppression device (6) is arranged on the vehicle-mounted device (1), and the material feeding device (7) is used for receiving and conveying materials dropped during excavation.

The invention discloses an RTBM construction method under a complex geological condition, belongs to the technical field of tunnel construction, and aims to solve the problems of aggravated cutter wear and low construction efficiency caused by delayed geological information acquisition and inaccurate construction parameter adjustment of a traditional hard rock tunnel boring machine under the complex geological condition. The invention provides a construction method based on multi-source geological data fusion and dynamic optimization. The method comprises the following steps: acquiring geological structure, rock texture and surrounding rock stress data in front of a tunnel face in real time through a multi-source geological detection device, classifying surrounding rock grades and identifying a fracture zone and a high-stress area by utilizing edge calculation nodes and combining a support vector machine algorithm, and dynamically adjusting the thrust, the rotating speed and the propelling speed of a cutterhead; meanwhile, the abrasion loss and the abrasion rate of the tool are monitored, and compensation optimization is conducted on the construction parameters based on a preset threshold value. The method can remarkably improve the safety of complex geological tunnel construction, prolong the service life of the cutter and improve the tunneling efficiency.

Perform MJS cavity filling reinforcement on the soil under the existing station floor, set the necessary cutter head using the hobcutter data obtained from the analysis, and set the tunneling parameters of the shield machine for cutting in the ground according to the tunneling parameters of the shield machine for cutting in the air. In addition to synchronous grouting injection and secondary grouting injection, perform the construction of secondary grouting injection when cutting the diaphragm wall, observe the vertical displacement of the ballast bed of the station, the vertical and horizontal displacements of the side wall of the station, and the vertical displacement of the tunnel vault, and adjust the push speed and silo pressure in real time. Perform the construction of secondary grouting injection on the segments within the range of 10 rings from the door of the receiving shaft of the newly built station. After the shield machine has passed through, perform injection from the grouting holes of the porous segments. 【Effect】It can save human resources and ensure the safety of the structure of the existing upper station.

The invention discloses a method for predicting shield tunneling parameters under a composite stratum, which comprises the following steps of: collecting and sorting shield tunneling historical data under the composite stratum, including geological parameters and construction parameters, and performing abnormal value elimination, missing value filling and data standardization; the geological parameters and the geometric parameters are combined according to the tunneling ring number, and a quantitative geological matrix of each ring is generated; the geometric parameters represent the position of the soil layer relative to the tunnel excavation face by adopting 1-5 codes, and input dimension unification is ensured through zero filling alignment; a CNN-Attention-SVR model is constructed, and the CNN-Attention-SVR model is trained; new geological data are input into the trained model, tunneling parameters are predicted, and construction adjustment is guided; reliable technical support and decision basis are provided for tunnel construction under the composite stratum condition, and therefore the overall safety and economical efficiency of shield construction are improved.

The invention provides a shield tunneling machine real-time data monitoring and operation efficiency improving method and system, and relates to the field of industrial equipment intelligent monitoring and optimal control. Multifunctional sensor modules are arranged on a cutter head system, a propelling system, a soil bin system and a shield tail system of a shield tunneling machine to collect operation parameters; and the local data processing unit is used for filtering and checking the data, so that the accuracy and reliability of the data are ensured. Based on a geological survey report, geological conditions are divided into multiple stratums, differential monitoring parameter combinations for different stratums are constructed, and dynamic monitoring and adjustment are achieved. And a tunneling speed optimization model based on the stratum discrimination function is further established, and the optimal tunneling speed is calculated in real time according to the dynamic change of the stratum characteristics, so that the tunneling efficiency of the shield tunneling machine is remarkably improved, the construction uncertainty is reduced, and the construction safety is improved.

The invention discloses a control method for adjusting the tunneling attitude of a shield tunneling machine, particularly relates to the field of attitude control of the shield tunneling machine, and aims to solve the problem that attitude deviation caused by underground environment change cannot be timely and effectively corrected in the tunneling process of the shield tunneling machine. The method comprises the following steps of: calculating membership degrees of horizontal deviation, vertical deviation and change rates of the horizontal deviation and the vertical deviation, extracting high-frequency and low-frequency characteristics, generating independent phase signals, and calculating a judgment coefficient by using a multi-dimensional data fusion and high-dimensional exponential integration method so as to dynamically adjust the weight of a fuzzy control rule base and realize adaptive optimization of PID (Proportion Integration Differentiation) gain parameters; a master control output signal is generated based on the updated PID parameters, the thrust of the partition oil cylinders is accurately adjusted, it is ensured that the posture of the shield tunneling machine is rapidly, accurately and stably adjusted under the complex environment condition, the control precision of the tunneling direction and the construction safety are improved, and the working efficiency is improved. And meanwhile, the overall posture correction capability of the shield tunneling machine is further enhanced through dynamic adjustment of the rolling angle.

The invention discloses a large-section long-footage mechanical blasting mixed excavation method suitable for a subway station, and relates to the technical field of underground rail transit engineering, and the method comprises the following steps: obtaining a tunnel section contour, a geologic structure and blasting parameters, building a three-dimensional blasting wave propagation inversion model, and combining geology and material heterogeneity to obtain a three-dimensional blasting wave propagation inversion model; and identifying a closed space region forming reflection interference, and predicting a space point location where blasting wave energy is abnormally accumulated. Based on the three-dimensional blasting wave propagation inversion model, the abnormal wave energy accumulation area is predicted in combination with geological heterogeneity, the high-risk blasting section is recognized, parameters are reconstructed, vibration real-time monitoring and model feedback correction are assisted, a closed-loop control system is constructed, local wave crest abnormity is effectively restrained, and the safety and controllability of large-section tunnel blasting construction are improved.

The invention belongs to the field of intelligent control systems, and particularly relates to a shield attitude real-time intelligent control method based on machine learning, according to the method, a data acquisition module can more comprehensively and accurately reflect the actual condition of shield construction, and in the control process, key parameters such as the working state of a jack and the pressure of a hydraulic system are monitored in real time, so that the control accuracy is improved. Abnormal conditions can be found in time, an emergency plan can be started, and the safety of shield construction is guaranteed. According to the real-time monitoring and feedback optimization mechanism, the safety and reliability of shield construction are effectively improved, and the construction risk is reduced.

The invention discloses a real-time dynamic optimization adjustment method and system for a tunneling process, and relates to the technical field of intelligent tunneling, and the method comprises the following steps: obtaining the operation state data of tunneling equipment in real time, and recording the dynamic change characteristics of a stratum; the tunneling parameters are optimized and adjusted in real time, the tunneling machine is prevented from entering a resonance interval, fatigue damage of key components such as a cutter head and a thrust cylinder is reduced, the stability of equipment is improved, and the service life of the equipment is prolonged. A multi-objective optimization control strategy is adopted, and an intelligent algorithm is combined, so that the tunneling mode is automatically adjusted, the tunneling efficiency is improved, and manual intervention is reduced. According to the stratum prediction method based on machine learning, the adaptability of the heading machine to complex stratums is enhanced, stratum sudden change risks are warned in advance, and construction potential safety hazards are reduced. And meanwhile, an abnormal state emergency processing mechanism ensures that the tunneling process is stable and controllable, and the construction safety and reliability are improved.

The invention discloses a shield tunneling machine axis predictive control method and system, electronic equipment and a storage medium, the shield tunneling machine axis predictive control method is based on data driving modeling, system dynamic characteristics contained in shield tunneling axis control related data are fully excavated, the predictive control precision is high, and the predictive control precision is high. A GRU (Gated Recurrent Neural Network) model and an ARX (Autoregressive Regressive) model with exogenous variables are combined to construct a guide deviation dynamic characteristic model to predict the guide deviation, the advantage that the GRU is good at processing a long-interval and long-delay time sequence is fully utilized, and the correlation characteristics before and after the shield guide deviation data time sequence are fully excavated; the model not only has the global nonlinear description capability, but also has the capability of describing the time sequence correlation characteristics of the system, the dynamic characteristics of the system are more comprehensively described, and the model is a nonlinear model which is more in line with the guiding actual working characteristics of the shield tunneling machine, and is higher in generalization capability and higher in precision.

The invention belongs to the technical field of hard rock cutting and tunneling, and particularly relates to a front eccentric block excitation cutting disc rock breaking device which comprises a cutting assembly, an eccentric excitation device, a driving assembly and a supporting assembly, and the rear portion of the supporting assembly is fixedly connected with a cutting arm of a tunneling machine; the driving assembly comprises a motor, a main shaft and various connecting devices; an eccentric excitation device and a cutting assembly are mounted at the front end of the main shaft; the cutting assembly is mounted at the front end of the main shaft; the driving device ensures stable rotation of the cutterhead and enables vibration generated by the eccentric excitation device to be transmitted to the cutterhead. Vibration generated by rotation of the eccentric block is utilized to enable the cutter head to impact the rock, so that the rock is collapsed, and the rock is efficiently cut; as the cutter head cuts the rock mainly by means of vibration impact generated by the eccentric device, compared with a self-rotating mode and a traditional milling rock breaking mode and a TBM rolling mode, the efficiency of cutting the rock is improved, and the service life of the cutter is prolonged.

Disclosed in the present invention are an underground gas storage device for compressed air energy storage and a construction method therefor. The underground gas storage device for compressed air energy storage comprises an underground gas storage cavern, the underground gas storage cavern being formed of a plurality of underground gas storage cavern sections, wherein a connecting tunnel is provided between two non-adjacent underground gas storage cavern sections, a vertical shaft is provided at the top end of the connecting tunnel, and a sealing plug is provided in the intersection between the vertical shaft and the connecting tunnel; a gas intake pipeline and a gas output pipeline are provided in the vertical shaft, and a sump is provided on an outer bottom wall of the connecting tunnel; a first drainage ditch is provided around the top face edge of the sealing plug in the vertical shaft, and the first drainage ditch is in communication with the sump; and a drainage blind pipe assembly is provided on the periphery of the underground gas storage cavern sections, and the drainage blind pipe assembly is in communication with the sump via a second drainage ditch arranged at the outer bottom wall of the connecting tunnel. The present invention can achieve one-time excavation forming, without transformation and with relatively low construction difficulty, and can effectively reduce the influence of underground water on the underground gas storage cavern during a construction period, operation period and maintenance period.

The invention discloses a dynamic geological survey system and a construction simulation method. The dynamic geological survey system comprises an intelligent sensing and data acquisition module and a multi-source data fusion processing module, the intelligent sensing and data acquisition module comprises a Raman scattering optical fiber strain sensor array, a three-component seismometer monitoring network and a distributed optical fiber pressure sensing subsystem; the Raman scattering optical fiber strain sensor array, the three-component seismometer monitoring network and the distributed optical fiber pressure sensing subsystem form a multi-dimensional sensing network for shield construction geological monitoring. By means of the integrated intelligent sensing and data acquisition module, geological changes in the shield construction process can be monitored. The Raman scattering optical fiber strain sensor array, the three-component seismometer monitoring network and the distributed optical fiber pressure sensing subsystem jointly form a multi-dimensional sensing network, geological strain, microseismic events, pore water pressure and other key information can be captured, and comprehensive geological data support is provided for construction.

The invention discloses a shield tunneling intelligent control method and system based on a ground-tunnel-machine-information-human adaptation level, and relates to the crossing field of underground engineering intelligent construction and information technology. Setting a first-level index and a second-level index; dividing the shield construction state into three levels of intelligent adaptation levels by combining the total score of the construction state with a dynamic threshold value; setting four targets of attitude intelligent control, settlement intelligent control, tunneling efficiency optimization and construction abnormity diagnosis; a machine learning prediction model is constructed, and the construction state parameters corresponding to the targets are predicted in real time; according to the shield intelligent adaptation level, the man-machine cooperation modes are divided into three classes of A level, B level and C level; and comparing actual data with a prediction result of the machine learning model, and dynamically correcting the control target parameters. Through real-time sensing of geological, mechanical and environmental multi-dimensional construction states and in combination with a dynamic weight distribution strategy, precise evaluation of the shield construction state is achieved, and meanwhile self-adaptive switching of the man-machine control right is achieved.